An approach for discovering keywords from Spanish tweets using Wikipedia

Daniel AYALA, Juan C. ROLDÁN, David RUIZ, Fernando O. GALLEGO


Most approaches to keywords discovery when analyzing microblogging messages (among them those from Twitter) are based on statistical and lexical information about the words that compose the text. The lack of context in the short messages can be problematic due to the low co-occurrence of words. In this paper, we present a new approach for keywords discovering from Spanish tweets based on the addition of context information using Wikipedia as a knowledge base. We present four different ways to use Wikipedia and two ways to rank the new keywords. We have tested these strategies using more than 60000 Spanish tweets, measuring performance and analyzing particularities of each strategy.


Twitter; Social Media Analysis; Wikipedia; Keywords Discovery

Full Text:



Blei, D. M., 2012. Probabilistic topic models. Communications of the ACM, 55(4):77–84.

Chen, Y., Li, Z., Nie, L., Hu, X., Wang, X., Chua, T.-s., and Zhang, X., 2012. A Semi-Supervised Bayesian Network Model for Microblog Topic Classification. In COLING, pages 561–576.

Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., and Ghosh, R., 2013. Discovering coherent topics

Dubhashi, D. P. and Panconesi, A., 2009. Concentration of measure for the analysis of randomized algorithms. Cambridge University Press.

Hennig-Thurau, T., Wiertz, C., and Feldhaus, F., 2014. Does Twitter matter? The impact of microblogging word of mouth on consumers' adoption of new movies. Journal of the Academy of Marketing Science, 43(3):375–394.

Hu, X. and Liu, H., 2012. Text analytics in social media. In Mining text data, pages 385–414. Springer.

Hulpus, I., Hayes, C., Karnstedt, M., and Greene, D., 2013. Unsupervised graph-based topic labelling using dbpedia. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 465–474. ACM.

Ko, Y., 2012. A study of term weighting schemes using class information for text classification. In Proceedings

Nenkova, A. and McKeown, K., 2012. A survey of text summarization techniques. In Mining Text Data, pages 43–76. Springer.

Ren, F. and Sohrab, M. G., 2013. Class-indexing-based term weighting for automatic text classification. Information Sciences, 236:109–125.

Thorleuchter, D. and Van den Poel, D., 2012. Improved multilevel security with latent semantic indexing. Expert Systems with Applications, 39(18):13462–13471.

Thorleuchter, D. and Van den Poel, D., 2013. Technology classification with latent semantic indexing. Expert

Systems with Applications, 40(5):1786–1795.

Xie, J., Emenheiser, J., Kirby, M., Sreenivasan, S., Szymanski, B., Holme, P. et al., 2012. Evolution of

Yubo Chen, S. F. and Wang, Q., 2011. The Role of Marketing in Social Media: How Online Consumer Reviews Evolve. Journal of Interactive Marketing, 25(2):85–94.

Zhang, W., Yoshida, T., and Tang, X., 2011. A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Systems with Applications, 38(3):2758–2765.

Zhu, J., Chen, N., Perkins, H., and Zhang, B., 2014. Gibbs max-margin topic models with data augmentation.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Clarivate Analytics